Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Sorting Permutations by Reversals and Eulerian Cycle Decompositions
SIAM Journal on Discrete Mathematics
New ideas in optimization
Memetic algorithms: a short introduction
New ideas in optimization
Fitness landscapes and memetic algorithm design
New ideas in optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
Selected Papers from the 4th European Conference on Artificial Evolution
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Path Tracing in Genetic Algorithms Applied to the Multiconstrained Knapsack Problem
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Direct Representation and Variation Operators for the Fixed Charge Transportation Problem
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On the Utility of Redundant Encodings in Mutation-Based Evolutionary Search
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms for the 0/1 Knapsack Problem
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Fitness Landscapes and Evolutionary Algorithms
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Characterizing Locality in Decoder-Based EAs for the Multidimensional Knapsack Problem
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Redundant representations in evolutionary computation
Evolutionary Computation
A hybrid approach for the 0-1 multidimensional knapsack problem
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Some novel locality results for the blob code spanning tree representation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The core concept for 0/1 integer programming
CATS '08 Proceedings of the fourteenth symposium on Computing: the Australasian theory - Volume 77
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Bringing order into the neighborhoods: relaxation guided variable neighborhood search
Journal of Heuristics
The Influence of Mutation on Protein-Ligand Docking Optimization: A Locality Analysis
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Parameter adjustment for genetic algorithm for two-level hierarchical covering location problem
WSEAS Transactions on Computers
On the bias and performance of the edge-set encoding
IEEE Transactions on Evolutionary Computation
Computing the metric dimension of graphs by genetic algorithms
Computational Optimization and Applications
The property analysis of evolutionary algorithms applied to spanning tree problems
Applied Intelligence
An ILP formulation and genetic algorithm for the Maximum Degree-Bounded Connected Subgraph problem
Computers & Mathematics with Applications
The Multidimensional Knapsack Problem: Structure and Algorithms
INFORMS Journal on Computing
Diversity improvement by non-geometric binary crossover in evolutionary multiobjective optimization
IEEE Transactions on Evolutionary Computation
Greedy algorithms for a class of knapsack problems with binary weights
Computers and Operations Research
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
On a property analysis of representations for spanning tree problems
EA'05 Proceedings of the 7th international conference on Artificial Evolution
The role of representations in dynamic knapsack problems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
The core concept for the multidimensional knapsack problem
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
An ecological approach to measuring locality in linear genotype to phenotype maps
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Countering the negative search bias of ant colony optimization in subset selection problems
Computers and Operations Research
Introducing graphical models to analyze genetic programming dynamics
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
The importance of the learning conditions in hyper-heuristics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Our main aim is to provide guidelines and practical help for the design of appropriate representations and operators for evolutionary algorithms (EAs). For this purpose, we propose techniques to obtain a better understanding of various effects in the interplay of the representation and the operators. We study six different representations and associated variation operators in the context of a steady-state evolutionary algorithm for the multidimensional knapsack problem. Four of them are indirect decoder-based techniques, and two are direct encodings combined with different initialization, repair, and local improvement strategies. The complex decoders and the local improvement and repair strategies make it practically impossible to completely analyze such EAs in a fully theoretical way. After comparing the general performance of the chosen EA variants for the multidimensional knapsack problem on two benchmark suites, we present a hands-on approach for empirically analyzing important aspects of initialization, mutation, and crossover in an isolated fashion. Static, inexpensive measurements based on randomly created solutions are performed in order to quantify and visualize specific properties with respect to heuristic bias, locality, and heritability. These tests shed light onto the complex behavior of such EAs and point out reasons for good or bad performance. In addition, the proposed measures are also examined during actual EA runs, which gives further insight into dynamic aspects of evolutionary search and verifies the validity of the isolated static measurements. All measurements are described in a general way, allowing for an easy adaption to other representations and problems.